The heterogeneous kernel-based convolutions algorithm was proposed by Pravendra Singh[1]}. Pravendra Singh et al presented a novel deep learning architecture in which the convolution operation uses heterogeneous kernels. Compared to standard convolution operations, the proposed HetConv reduces the number of calculations (FLOPs) and parameters while still maintaining the presentation efficiency. HetConv is especially different from the depthwise convolutional filter used to perform depthwise convolution (DWC)[2]}, the pointwise convolutional filter used to perform pointwise convolution (PWC)[3]} and the groupwise convolutional filter used to perform groupwise convolution (GWC)[4]}. In HetConv, a variable \(P\)  is used to control how much of the normal convolution kernel is retained in the operation. In addition, the total reduction is \(R\)  for \(K \times K\)  kernels. The number of calculations of HetConv is compared with that of the normal convolution, as shown in Eq REF .
\(R_{{HetConv}}=\frac{1}{P}+\frac{(1-1 / P)}{K^{2}}\) 
